In [7]:
#Poster of Tag
Image(path+"\\Sharkimgtitle.jpg",height=420,width=1000)
Out[7]:
#Poster of Tag
Image(path+"\\Sharkimgtitle.jpg",height=420,width=1000)
text =" Shark Tank India ".join(cat for cat in df['Episode Title'])
stop_words = list(STOPWORDS) + ["Ka", "Ki", "Ko","Karne","Wale","Aam","Aur","Ek"]
wordcloud = WordCloud(width=2000, height=1500, stopwords=stop_words, background_color='black', colormap='rocket_r', collocations=False, random_state=2022).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
# Judges of Sharks Tank image:
Image(path+"\\Sharksimage.jpg")
pwd
'E:\\DataScience\\MachineLearning\\Shark Tank Data Analysis'
path='E:\\DataScience\\MachineLearning\\Shark Tank Data Analysis'
import os
os.listdir(path)
['.ipynb_checkpoints', 'Analysis.ipynb', 'Project Approval Form BT3425.docx.pdf', 'Shark Tank India.csv', 'Sharkimgtitle.jpg', 'Sharksimage.jpg', 'Shark_Tank_India_data_SE_01.zip']
import os
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
import plotly.express as px
import pandas as pd
import numpy as np
from scipy import signal
from wordcloud import WordCloud, STOPWORDS
from babel.numbers import format_currency
import plotly.io as pio
pio.templates.default = "plotly_dark"
#to supress warning
import warnings
warnings.filterwarnings('ignore')
#to make shell more intractive
from IPython.display import display
from IPython.display import Image
# setting up the chart size and background
plt.rcParams['figure.figsize'] = (16, 8)
plt.style.use('fivethirtyeight')
df=pd.read_csv(path+"\\Shark Tank India.csv")
df.head(15).style.background_gradient(cmap = 'Oranges')
| Season Number | Episode Number | Episode Title | Pitch Number | Startup Name | Industry | Business Description | Company Website | Number of Presenters | Male Presenters | Female Presenters | Couple Presenters | Pitchers Average Age | Started in | Pitchers City | Pitchers State | Yearly Revenue | Monthly Sales | Gross Margin | Original Ask Amount | Original Ask Equity | Valuation Requested | Received Offer | Accepted Offer | Total Deal Amount | Total Deal Equity | Total Deal Debt | Valuation Offered | Ashneer Investment Amount | Ashneer Investment Equity | Ashneer Debt Amount | Namita Investment Amount | Namita Investment Equity | Namita Debt Amount | Anupam Investment Amount | Anupam Investment Equity | Anupam Debt Amount | Vineeta Investment Amount | Vineeta Investment Equity | Vineeta Debt Amount | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Ghazal Investment Amount | Ghazal Investment Equity | Ghazal Debt Amount | Number of sharks in deal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | Badlegi Business Ki Tasveer | 1 | BluePine Foods | Food | Frozen Momos | https://bluepinefoods.com/ | 3 | 2.000000 | 1.000000 | 0.000000 | Middle | 2016.000000 | Delhi | Delhi | 95.000000 | 800000.000000 | nan | 50.000000 | 5.000000 | 1000 | 1 | 1.000000 | 75.000000 | 16.000000 | nan | 469.000000 | 25.000000 | 5.330000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 25.000000 | 5.330000 | nan | 25.000000 | 5.330000 | nan | nan | nan | nan | nan | nan | nan | 3.000000 |
| 1 | 1 | 1 | Badlegi Business Ki Tasveer | 2 | Booz Scooters | Electrical Vehicles | Renting e-bike for mobility in private spaces | https://www.boozup.net/ | 1 | 1.000000 | nan | 0.000000 | Young | 2017.000000 | Ahmedabad | Gujarat | 4.000000 | 40000.000000 | nan | 40.000000 | 15.000000 | 267 | 1 | 1.000000 | 40.000000 | 50.000000 | nan | 80.000000 | 20.000000 | 25.000000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 20.000000 | 25.000000 | nan | 0.000000 | 0.000000 | nan | nan | nan | nan | nan | nan | nan | 2.000000 |
| 2 | 1 | 1 | Badlegi Business Ki Tasveer | 3 | Heart up my Sleeves | Beauty/Fashion | Detachable Sleeves | https://heartupmysleeves.com/ | 1 | nan | 1.000000 | 0.000000 | Young | 2021.000000 | Delhi | Delhi | nan | 200000.000000 | nan | 25.000000 | 10.000000 | 250 | 1 | 1.000000 | 25.000000 | 30.000000 | nan | 83.000000 | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 12.500000 | 15.000000 | nan | 12.500000 | 15.000000 | nan | 0.000000 | 0.000000 | nan | nan | nan | nan | nan | nan | nan | 2.000000 |
| 3 | 1 | 2 | Insaan, Ideas Aur Sapne | 4 | Tagz Foods | Food | Healthy Potato Chips Snacks | https://tagzfoods.com/ | 2 | 2.000000 | nan | 0.000000 | Middle | 2019.000000 | Bangalore | Karnataka | 700.000000 | nan | 48.000000 | 70.000000 | 1.000000 | 7000 | 1 | 1.000000 | 70.000000 | 2.750000 | nan | 2545.000000 | 70.000000 | 2.750000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | nan | nan | nan | nan | nan | nan | 1.000000 |
| 4 | 1 | 2 | Insaan, Ideas Aur Sapne | 5 | Head and Heart | Education | Brain Development Course | https://thehnh.in/ | 4 | 1.000000 | 3.000000 | 1.000000 | Middle | 2015.000000 | nan | Punjab | 30.000000 | nan | nan | 50.000000 | 5.000000 | 1000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 5 | 1 | 2 | Insaan, Ideas Aur Sapne | 6 | Agri tourism | Services | Tourism | https://www.agritourism.in/ | 2 | 1.000000 | 1.000000 | 1.000000 | Middle | 2005.000000 | Baramati | Maharashtra | 79.000000 | nan | nan | 50.000000 | 5.000000 | 1000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 6 | 1 | 3 | Aam Aadmi Ke Business Ideas | 7 | qZense Labs | Food | Food Freshness Detector | https://www.qzense.com/ | 2 | nan | 2.000000 | 0.000000 | Middle | 2020.000000 | Mohali,Delhi | Punjab, Delhi | 25.000000 | nan | nan | 100.000000 | 0.250000 | 40000 | 1 | 0.000000 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 7 | 1 | 3 | Aam Aadmi Ke Business Ideas | 8 | Peeschute | Beauty/Fashion | Disposable Urine Bag | https://www.peeschute.com/ | 1 | 1.000000 | nan | 0.000000 | Young | 2019.000000 | Jalna | Maharashtra | 100.000000 | nan | nan | 75.000000 | 4.000000 | 1875 | 1 | 1.000000 | 75.000000 | 6.000000 | nan | 1250.000000 | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 75.000000 | 6.000000 | nan | nan | nan | nan | nan | nan | nan | 1.000000 |
| 8 | 1 | 3 | Aam Aadmi Ke Business Ideas | 9 | NOCD | Food | Energy Drink | nan | 2 | 2.000000 | nan | 0.000000 | Middle | 2019.000000 | Bangalore | Karnataka | nan | 2000000.000000 | nan | 50.000000 | 2.000000 | 2500 | 1 | 1.000000 | 20.000000 | 15.000000 | 30.000000 | 133.000000 | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 20.000000 | 15.000000 | 30.000000 | 0.000000 | 0.000000 | nan | nan | nan | nan | nan | nan | nan | 1.000000 |
| 9 | 1 | 4 | Entrepreneurship Ki Wave | 10 | Cos IQ | Beauty/Fashion | Intelligent Skincare | https://mycosiq.com/ | 2 | 1.000000 | 1.000000 | 1.000000 | Middle | 2021.000000 | Delhi | Delhi | nan | 400000.000000 | 75.000000 | 50.000000 | 7.500000 | 667 | 1 | 1.000000 | 50.000000 | 25.000000 | nan | 200.000000 | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 25.000000 | 12.500000 | nan | 25.000000 | 12.500000 | nan | 0.000000 | 0.000000 | nan | nan | nan | nan | nan | nan | nan | 2.000000 |
| 10 | 1 | 4 | Entrepreneurship Ki Wave | 11 | JhaJi Achaar | Food | Pickle | https://www.jhajistore.com/ | 2 | nan | 2.000000 | 0.000000 | Old | 2021.000000 | Darbhanga | Bihar | nan | 500000.000000 | 18.000000 | 50.000000 | 10.000000 | 500 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 11 | 1 | 4 | Entrepreneurship Ki Wave | 12 | Bummer | Beauty/Fashion | Underwear | https://bummer.in/ | 1 | 1.000000 | nan | 0.000000 | Young | 2020.000000 | Ahmedabad | Gujarat | nan | 1500000.000000 | 70.000000 | 75.000000 | 4.000000 | 1875 | 1 | 1.000000 | 75.000000 | 7.500000 | nan | 1000.000000 | 0.000000 | 0.000000 | nan | 37.500000 | 3.750000 | nan | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 37.500000 | 3.750000 | nan | nan | nan | nan | nan | nan | nan | 2.000000 |
| 12 | 1 | 5 | Hunt For Interesting Business | 13 | Revamp Moto | Electrical Vehicles | E-Bike Mitra bud-e RM | https://www.revampmoto.in/ | 3 | 3.000000 | nan | 0.000000 | Young | nan | Nashik | Maharashtra | nan | 1000.000000 | nan | 100.000000 | 1.000000 | 10000 | 1 | 1.000000 | 100.000000 | 1.500000 | nan | 6667.000000 | 0.000000 | 0.000000 | nan | 0.000000 | 0.000000 | nan | 50.000000 | 0.750000 | nan | 0.000000 | 0.000000 | nan | 50.000000 | 0.750000 | nan | nan | nan | nan | nan | nan | nan | 2.000000 |
| 13 | 1 | 5 | Hunt For Interesting Business | 14 | Hungry Head | Food | Restaurant serving 80 types of Maggi | nan | 2 | 2.000000 | nan | 0.000000 | Middle | 2013.000000 | Mumbai | Maharashtra | nan | 550000.000000 | nan | 50.000000 | 5.000000 | 1000 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 14 | 1 | 5 | Hunt For Interesting Business | 15 | Shrawani Engineers | Beauty/Fashion | Belly Button Shaper | https://www.shrawaniengineers.com/ | 2 | 1.000000 | 1.000000 | 1.000000 | Middle | 2019.000000 | Nagpur | Maharashtra | 50.000000 | nan | nan | 20.000000 | 10.000000 | 200 | 0 | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
df['Male Presenters'] = df['Male Presenters'].astype(pd.Int32Dtype())
df['Female Presenters'] = df['Female Presenters'].astype(pd.Int32Dtype())
df['Started in'] = df['Started in'].astype(pd.Int32Dtype())
df['Yearly Revenue'] = df['Yearly Revenue'].astype(pd.Int32Dtype())
df['Monthly Sales'] = df['Monthly Sales'].astype(pd.Int32Dtype())
df['Valuation Offered'] = df['Valuation Offered'].astype(pd.Int32Dtype())
df.head(5)
| Season Number | Episode Number | Episode Title | Pitch Number | Startup Name | Industry | Business Description | Company Website | Number of Presenters | Male Presenters | ... | Aman Investment Amount | Aman Investment Equity | Aman Debt Amount | Peyush Investment Amount | Peyush Investment Equity | Peyush Debt Amount | Ghazal Investment Amount | Ghazal Investment Equity | Ghazal Debt Amount | Number of sharks in deal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | Badlegi Business Ki Tasveer | 1 | BluePine Foods | Food | Frozen Momos | https://bluepinefoods.com/ | 3 | 2 | ... | 25.0 | 5.33 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.0 |
| 1 | 1 | 1 | Badlegi Business Ki Tasveer | 2 | Booz Scooters | Electrical Vehicles | Renting e-bike for mobility in private spaces | https://www.boozup.net/ | 1 | 1 | ... | 0.0 | 0.00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 |
| 2 | 1 | 1 | Badlegi Business Ki Tasveer | 3 | Heart up my Sleeves | Beauty/Fashion | Detachable Sleeves | https://heartupmysleeves.com/ | 1 | <NA> | ... | 0.0 | 0.00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.0 |
| 3 | 1 | 2 | Insaan, Ideas Aur Sapne | 4 | Tagz Foods | Food | Healthy Potato Chips Snacks | https://tagzfoods.com/ | 2 | 2 | ... | 0.0 | 0.00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.0 |
| 4 | 1 | 2 | Insaan, Ideas Aur Sapne | 5 | Head and Heart | Education | Brain Development Course | https://thehnh.in/ | 4 | 1 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 50 columns
df.shape
(121, 50)
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 121 entries, 0 to 120 Data columns (total 50 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season Number 121 non-null int64 1 Episode Number 121 non-null int64 2 Episode Title 121 non-null object 3 Pitch Number 121 non-null int64 4 Startup Name 121 non-null object 5 Industry 121 non-null object 6 Business Description 121 non-null object 7 Company Website 49 non-null object 8 Number of Presenters 121 non-null int64 9 Male Presenters 102 non-null Int32 10 Female Presenters 62 non-null Int32 11 Couple Presenters 102 non-null float64 12 Pitchers Average Age 55 non-null object 13 Started in 65 non-null Int32 14 Pitchers City 85 non-null object 15 Pitchers State 86 non-null object 16 Yearly Revenue 49 non-null Int32 17 Monthly Sales 61 non-null Int32 18 Gross Margin 35 non-null float64 19 Original Ask Amount 121 non-null float64 20 Original Ask Equity 121 non-null float64 21 Valuation Requested 121 non-null int64 22 Received Offer 121 non-null int64 23 Accepted Offer 88 non-null float64 24 Total Deal Amount 67 non-null float64 25 Total Deal Equity 67 non-null float64 26 Total Deal Debt 9 non-null float64 27 Valuation Offered 67 non-null Int32 28 Ashneer Investment Amount 54 non-null float64 29 Ashneer Investment Equity 54 non-null float64 30 Ashneer Debt Amount 2 non-null float64 31 Namita Investment Amount 62 non-null float64 32 Namita Investment Equity 62 non-null float64 33 Namita Debt Amount 1 non-null float64 34 Anupam Investment Amount 67 non-null float64 35 Anupam Investment Equity 67 non-null float64 36 Anupam Debt Amount 1 non-null float64 37 Vineeta Investment Amount 34 non-null float64 38 Vineeta Investment Equity 34 non-null float64 39 Vineeta Debt Amount 1 non-null float64 40 Aman Investment Amount 56 non-null float64 41 Aman Investment Equity 56 non-null float64 42 Aman Debt Amount 1 non-null float64 43 Peyush Investment Amount 53 non-null float64 44 Peyush Investment Equity 53 non-null float64 45 Peyush Debt Amount 5 non-null float64 46 Ghazal Investment Amount 13 non-null float64 47 Ghazal Investment Equity 13 non-null float64 48 Ghazal Debt Amount 0 non-null float64 49 Number of sharks in deal 67 non-null float64 dtypes: Int32(6), float64(30), int64(6), object(8) memory usage: 45.3+ KB
df.describe().T.round(2).style.background_gradient(cmap = 'Oranges')
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Season Number | 121.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Episode Number | 121.000000 | 19.310000 | 10.380000 | 1.000000 | 11.000000 | 19.000000 | 28.000000 | 36.000000 |
| Pitch Number | 121.000000 | 61.000000 | 35.070000 | 1.000000 | 31.000000 | 61.000000 | 91.000000 | 121.000000 |
| Number of Presenters | 121.000000 | 2.070000 | 0.920000 | 1.000000 | 1.000000 | 2.000000 | 3.000000 | 6.000000 |
| Male Presenters | 102.000000 | 1.730000 | 0.970000 | 1.000000 | 1.000000 | 1.000000 | 2.000000 | 6.000000 |
| Female Presenters | 62.000000 | 1.210000 | 0.480000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 3.000000 |
| Couple Presenters | 102.000000 | 0.170000 | 0.370000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 |
| Started in | 65.000000 | 2018.280000 | 2.610000 | 2005.000000 | 2017.000000 | 2019.000000 | 2020.000000 | 2021.000000 |
| Yearly Revenue | 49.000000 | 422.590000 | 1084.520000 | 0.000000 | 60.000000 | 115.000000 | 250.000000 | 7200.000000 |
| Monthly Sales | 61.000000 | 1550052.460000 | 3258030.400000 | 0.000000 | 200000.000000 | 600000.000000 | 1600000.000000 | 20000000.000000 |
| Gross Margin | 35.000000 | 53.740000 | 28.930000 | 3.000000 | 30.000000 | 50.000000 | 70.000000 | 150.000000 |
| Original Ask Amount | 121.000000 | 312.590000 | 2721.620000 | 0.000000 | 45.000000 | 50.000000 | 80.000000 | 30000.000000 |
| Original Ask Equity | 121.000000 | 4.860000 | 3.690000 | 0.250000 | 2.000000 | 4.000000 | 7.000000 | 25.000000 |
| Valuation Requested | 121.000000 | 4414.520000 | 12391.060000 | 0.000000 | 667.000000 | 1500.000000 | 3250.000000 | 120000.000000 |
| Received Offer | 121.000000 | 0.730000 | 0.450000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 |
| Accepted Offer | 88.000000 | 0.760000 | 0.430000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| Total Deal Amount | 67.000000 | 58.250000 | 30.470000 | 0.000000 | 40.000000 | 50.000000 | 75.000000 | 150.000000 |
| Total Deal Equity | 67.000000 | 15.890000 | 13.800000 | 1.000000 | 5.500000 | 15.000000 | 20.500000 | 75.000000 |
| Total Deal Debt | 9.000000 | 39.000000 | 25.110000 | 20.000000 | 25.000000 | 30.000000 | 50.000000 | 99.000000 |
| Valuation Offered | 67.000000 | 850.400000 | 1086.040000 | 0.000000 | 163.500000 | 500.000000 | 1125.000000 | 6667.000000 |
| Ashneer Investment Amount | 54.000000 | 9.990000 | 16.340000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | 70.000000 |
| Ashneer Investment Equity | 54.000000 | 1.730000 | 3.800000 | 0.000000 | 0.000000 | 0.000000 | 2.380000 | 25.000000 |
| Ashneer Debt Amount | 2.000000 | 57.000000 | 59.400000 | 15.000000 | 36.000000 | 57.000000 | 78.000000 | 99.000000 |
| Namita Investment Amount | 62.000000 | 10.970000 | 18.540000 | 0.000000 | 0.000000 | 0.000000 | 18.750000 | 75.000000 |
| Namita Investment Equity | 62.000000 | 2.270000 | 4.710000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 25.000000 |
| Namita Debt Amount | 1.000000 | 25.000000 | nan | 25.000000 | 25.000000 | 25.000000 | 25.000000 | 25.000000 |
| Anupam Investment Amount | 67.000000 | 7.970000 | 13.390000 | 0.000000 | 0.000000 | 0.000000 | 15.420000 | 50.000000 |
| Anupam Investment Equity | 67.000000 | 2.480000 | 4.790000 | 0.000000 | 0.000000 | 0.000000 | 1.880000 | 17.500000 |
| Anupam Debt Amount | 1.000000 | 15.000000 | nan | 15.000000 | 15.000000 | 15.000000 | 15.000000 | 15.000000 |
| Vineeta Investment Amount | 34.000000 | 9.860000 | 12.280000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | 40.000000 |
| Vineeta Investment Equity | 34.000000 | 3.990000 | 5.980000 | 0.000000 | 0.000000 | 0.000000 | 5.250000 | 25.000000 |
| Vineeta Debt Amount | 1.000000 | 30.000000 | nan | 30.000000 | 30.000000 | 30.000000 | 30.000000 | 30.000000 |
| Aman Investment Amount | 56.000000 | 15.970000 | 22.950000 | 0.000000 | 0.000000 | 3.500000 | 25.000000 | 100.000000 |
| Aman Investment Equity | 56.000000 | 2.930000 | 5.810000 | 0.000000 | 0.000000 | 0.880000 | 4.250000 | 40.000000 |
| Aman Debt Amount | 1.000000 | 50.000000 | nan | 50.000000 | 50.000000 | 50.000000 | 50.000000 | 50.000000 |
| Peyush Investment Amount | 53.000000 | 14.900000 | 21.190000 | 0.000000 | 0.000000 | 8.330000 | 25.000000 | 100.000000 |
| Peyush Investment Equity | 53.000000 | 6.000000 | 13.630000 | 0.000000 | 0.000000 | 1.000000 | 5.000000 | 75.000000 |
| Peyush Debt Amount | 5.000000 | 23.400000 | 2.300000 | 20.000000 | 22.000000 | 25.000000 | 25.000000 | 25.000000 |
| Ghazal Investment Amount | 13.000000 | 10.000000 | 12.380000 | 0.000000 | 0.000000 | 0.000000 | 20.000000 | 33.330000 |
| Ghazal Investment Equity | 13.000000 | 3.590000 | 5.320000 | 0.000000 | 0.000000 | 1.000000 | 5.000000 | 17.500000 |
| Ghazal Debt Amount | 0.000000 | nan | nan | nan | nan | nan | nan | nan |
| Number of sharks in deal | 67.000000 | 2.220000 | 1.170000 | 1.000000 | 1.000000 | 2.000000 | 3.000000 | 5.000000 |
df.columns
Index(['Season Number', 'Episode Number', 'Episode Title', 'Pitch Number',
'Startup Name', 'Industry', 'Business Description', 'Company Website',
'Number of Presenters', 'Male Presenters', 'Female Presenters',
'Couple Presenters', 'Pitchers Average Age', 'Started in',
'Pitchers City', 'Pitchers State', 'Yearly Revenue', 'Monthly Sales',
'Gross Margin', 'Original Ask Amount', 'Original Ask Equity',
'Valuation Requested', 'Received Offer', 'Accepted Offer',
'Total Deal Amount', 'Total Deal Equity', 'Total Deal Debt',
'Valuation Offered', 'Ashneer Investment Amount',
'Ashneer Investment Equity', 'Ashneer Debt Amount',
'Namita Investment Amount', 'Namita Investment Equity',
'Namita Debt Amount', 'Anupam Investment Amount',
'Anupam Investment Equity', 'Anupam Debt Amount',
'Vineeta Investment Amount', 'Vineeta Investment Equity',
'Vineeta Debt Amount', 'Aman Investment Amount',
'Aman Investment Equity', 'Aman Debt Amount',
'Peyush Investment Amount', 'Peyush Investment Equity',
'Peyush Debt Amount', 'Ghazal Investment Amount',
'Ghazal Investment Equity', 'Ghazal Debt Amount',
'Number of sharks in deal'],
dtype='object')
print("Shark Tank Episodes are Boradcasted on SonyLiv OTT Platform")
print("Season :",df['Season Number'].max())
print("Total number of Episodes in Covered:",df['Episode Number'].max())
print("Total number of Pitch :",df['Pitch Number'].max())
Shark Tank Episodes are Boradcasted on SonyLiv OTT Platform Season : 1 Total number of Episodes in Covered: 36 Total number of Pitch : 121
df['Industry'].value_counts()
Food 37 Beauty/Fashion 22 Manufacturing 17 Technology 11 Education 8 Services 7 Medical 7 Electrical Vehicles 4 Animal/Pets 3 Hardware 3 Sports 2 Name: Industry, dtype: int64
df['Industry'].value_counts().index
Index(['Food', 'Beauty/Fashion', 'Manufacturing', 'Technology', 'Education',
'Services', 'Medical', 'Electrical Vehicles', 'Animal/Pets', 'Hardware',
'Sports'],
dtype='object')
fig = px.bar(df,x=df['Industry'].value_counts().index,y=df['Industry'].value_counts(),
color=df['Industry'].value_counts().index,title="Types of Industry")
fig.update_xaxes(title_text='Industry')
fig.update_yaxes(title_text='No. of Startsups in those Sector')
fig.show()
print("Total number of Presenters :",df['Number of Presenters'].sum(),"\n")
print("Total number of Male Presenters :",df['Male Presenters'].sum(),"\n")
print("Total number of Female Presenters :",df['Female Presenters'].sum(),"\n")
print("Male Entrepreneurs % :",round(df['Male Presenters'].sum()/df['Number of Presenters'].sum()*100, 0),"\n")
print("Female Entrepreneurs % :",round(df['Female Presenters'].sum()/df['Number of Presenters'].sum()*100, 0),"\n")
Total number of Presenters : 251 Total number of Male Presenters : 176 Total number of Female Presenters : 75 Male Entrepreneurs % : 70.0 Female Entrepreneurs % : 30.0
print("Total number of Couple Presenters :",df['Couple Presenters'].sum(),"\n")
print("Couple Entrepreneurs % :",round(df['Couple Presenters'].sum()/df['Number of Presenters'].sum()*100, 0),"\n")
Total number of Couple Presenters : 17.0 Couple Entrepreneurs % : 7.0
print(df['Pitchers State'].value_counts())
fig = px.bar(df,x=df['Pitchers State'].value_counts().index,y=df['Pitchers State'].value_counts(),
color=df['Pitchers State'].value_counts().index,title="Pitchers State")
fig.update_xaxes(title_text='State')
fig.update_yaxes(title_text='No. of Startups')
fig.show()
Maharashtra 26 Delhi 13 Gujarat 11 Karnataka 6 Telangana 5 West Bengal 4 Uttar Pradesh 4 Punjab 2 Jammu & Kashmir 2 Tamil Nadu 2 Kerala 2 Goa 1 Maharashtra, Delhi 1 Punjab, Delhi 1 Madhya Pradesh 1 Karnataka, West Bengal 1 Rajasthan 1 Uttarakhand 1 Haryana 1 Bihar 1 Name: Pitchers State, dtype: int64
fig = px.pie(df, values=df['Pitchers State'].value_counts(), names=df['Pitchers State'].value_counts().index
, title='State % Share of Entrepreneurs:')
fig.show()
#Average age type value count
df['Pitchers Average Age'].value_counts()
Middle 34 Young 20 Old 1 Name: Pitchers Average Age, dtype: int64
# Most of the Avg age of Entrepreneurs is null
df['Pitchers Average Age'].isnull().sum()
66
## top 20 city from where the most pitches came..
df['Pitchers City'].value_counts().nlargest(20)
Mumbai 14 Delhi 13 Pune 6 Hyderabad 5 Ahmedabad 5 Bangalore 5 Kolkata 4 Thiruvananthapuram 2 Gandhinagar 2 Surat 2 Nagpur 2 Jammu 2 Mohali,Delhi 1 Jalna 1 Malegaon 1 Vadodara 1 Valsad 1 Pune, Delhi 1 Modinagar 1 Jaipur 1 Name: Pitchers City, dtype: int64
fig = px.pie(df, values=df['Pitchers City'].value_counts().nlargest(15), names=df['Pitchers City'].value_counts().nlargest(15).index
, title='Startup Distribution Top 15 Cities ')
fig.show()
print(df.groupby('Startup Name')['Yearly Revenue'].max().nlargest(15))
fig = px.bar(df,x=df.groupby('Startup Name')['Yearly Revenue'].max().nlargest(15).index,y=df.groupby('Startup Name')['Yearly Revenue'].max().nlargest(15),
color=df.groupby('Startup Name')['Yearly Revenue'].max().nlargest(15).index,title="Top 15 Startups which generates highest Revenue [in Lakhs]")
fig.update_xaxes(title_text='Startups')
fig.update_yaxes(title_text='Yearly Revenue (lakhs)')
fig.show()
Startup Name French Crown 7200 Guardian Gears 2500 Raising Superstars 1300 PlayBoxTV 1020 Alpino 1000 Hammer Lifestyle 1000 Shades of Spring 900 Tagz Foods 700 Devnagri 500 Moonshine 372 Ariro 300 PDD Falcon 272 Kabaddi Adda 250 RoadBounce 250 Humpy A2 208 Name: Yearly Revenue, dtype: Int32
print(df.groupby('Startup Name')['Gross Margin'].max().nlargest(15))
fig = px.bar(df,x=df.groupby('Startup Name')['Gross Margin'].max().nlargest(15).index,y=df.groupby('Startup Name')['Gross Margin'].max().nlargest(15),
color=df.groupby('Startup Name')['Gross Margin'].max().nlargest(15).index,title="Top 15 Startups which have highest Gross Margin [in Lakhs]")
fig.update_xaxes(title_text='Startups')
fig.update_yaxes(title_text='Gross Margin (lakhs/Yearly)')
fig.show()
Startup Name Poo-de-Cologne 150.0 Farda 115.0 Cocofit 95.0 Auli 80.0 Cos IQ 75.0 Thea and Sid 75.0 Bummer 70.0 French Crown 70.0 Moonshine 70.0 Nomad Food Project 70.0 Nuutjob 70.0 Get-A-Whey 69.0 The Sass Bar 65.0 Hair Originals 62.0 Let's Try 55.0 Name: Gross Margin, dtype: float64
print(df.groupby('Startup Name')['Monthly Sales'].max().nlargest(15))
fig = px.bar(df,x=df.groupby('Startup Name')['Monthly Sales'].max().nlargest(15).index,y=df.groupby('Startup Name')['Monthly Sales'].max().nlargest(15),
color=df.groupby('Startup Name')['Monthly Sales'].max().nlargest(15).index,title="Top 15 Startups which have highest Gross Margin [in Millons]")
fig.update_xaxes(title_text='Startups')
fig.update_yaxes(title_text='Monthly Sales')
fig.show()
Startup Name Urban Monkey 20000000 Kabira Handmad 16000000 Hair Originals 5000000 The State Plate 4000000 Gopal's 56 3300000 Get-A-Whey 3000000 Kunafa World 3000000 Ariro 2500000 Insurance Samadhan 2200000 Guardian Gears 2100000 Beyond Snack 2060000 Bamboo India 2000000 NOCD 2000000 PawsIndia 2000000 Astrix 1800000 Name: Monthly Sales, dtype: Int32
print(df.isnull().sum())
sns.heatmap(df.isnull())
Season Number 0 Episode Number 0 Episode Title 0 Pitch Number 0 Startup Name 0 Industry 0 Business Description 0 Company Website 72 Number of Presenters 0 Male Presenters 19 Female Presenters 59 Couple Presenters 19 Pitchers Average Age 66 Started in 56 Pitchers City 36 Pitchers State 35 Yearly Revenue 72 Monthly Sales 60 Gross Margin 86 Original Ask Amount 0 Original Ask Equity 0 Valuation Requested 0 Received Offer 0 Accepted Offer 33 Total Deal Amount 54 Total Deal Equity 54 Total Deal Debt 112 Valuation Offered 54 Ashneer Investment Amount 67 Ashneer Investment Equity 67 Ashneer Debt Amount 119 Namita Investment Amount 59 Namita Investment Equity 59 Namita Debt Amount 120 Anupam Investment Amount 54 Anupam Investment Equity 54 Anupam Debt Amount 120 Vineeta Investment Amount 87 Vineeta Investment Equity 87 Vineeta Debt Amount 120 Aman Investment Amount 65 Aman Investment Equity 65 Aman Debt Amount 120 Peyush Investment Amount 68 Peyush Investment Equity 68 Peyush Debt Amount 116 Ghazal Investment Amount 108 Ghazal Investment Equity 108 Ghazal Debt Amount 121 Number of sharks in deal 54 dtype: int64
<AxesSubplot:>
print(df.groupby('Startup Name')['Original Ask Amount'].max().nlargest(15))
Startup Name Gopal's 56 30000.0 Shades of Spring 300.0 ExperentialEtc 200.0 Aas Vidyalaya 150.0 Alpino 150.0 French Crown 150.0 Keto India 150.0 Magic lock 120.0 Devnagri 100.0 Elcare India 100.0 Get-A-Whey 100.0 Hecoll 100.0 Insurance Samadhan 100.0 Kabira Handmad 100.0 Morriko Pure Foods 100.0 Name: Original Ask Amount, dtype: float64
fig = px.bar(df,x=df.groupby('Startup Name')['Original Ask Amount'].max().nlargest(15).index,y=df.groupby('Startup Name')['Original Ask Amount'].max().nlargest(15),
color=df.groupby('Startup Name')['Original Ask Amount'].max().nlargest(15).index,title="Top 15 Startups which ask for highest amount [in Lakhs]")
fig.update_xaxes(title_text='Startups')
fig.update_yaxes(title_text='Original Ask Amount')
fig.show()
#Total Shark Deal:
total_shark=df[df["Number of sharks in deal"]==5]
figure=px.bar(total_shark, x='Startup Name', y='Total Deal Amount',title="Five Shark Deal Brands and the total investment:",text_auto=True, color='Original Ask Amount',
template="plotly_dark")
figure.show()
print(df.groupby('Startup Name')['Original Ask Amount'].min().nsmallest(15))
Startup Name Cocofit 0.00005 Watt Technovations 0.00101 Ethik 15.00000 Nuskha Kitchen 20.00000 Shrawani Engineers 20.00000 SneaKare 20.00000 Heart up my Sleeves 25.00000 Nuutjob 25.00000 Annie 30.00000 EventBeep 30.00000 Farda 30.00000 Guardian Gears 30.00000 Hammer Lifestyle 30.00000 KG Agrotech 30.00000 Meatyour 30.00000 Name: Original Ask Amount, dtype: float64
fig = px.bar(df,x=df.groupby('Startup Name')['Original Ask Amount'].min().nsmallest(15).index,y=df.groupby('Startup Name')['Original Ask Amount'].max().nlargest(15),
color=df.groupby('Startup Name')['Original Ask Amount'].min().nsmallest(15).index,title="Top 15 Startups which ask for lowest amount [in Lakhs]")
fig.update_xaxes(title_text='Startups')
fig.update_yaxes(title_text='Original Ask Amount')
fig.show()
#Ask Equity and Deal Equity of Highest Pitch Ask AMount Brand
figure=px.bar(df, x='Startup Name', y='Original Ask Equity',title="Ask Equity & Deal Equity of Highest Pitch Ask Amount Brand:",text_auto=True, color='Total Deal Equity',)
figure.show()
#the number of investments done by individual shark
Ashneer_amount=df.loc[(df["Ashneer Investment Amount"].isnull()==False)&(df["Ashneer Investment Amount"]!=0)]
Namita_amount=df.loc[(df["Namita Investment Amount"].isnull()==False)&(df["Namita Investment Amount"]!=0)]
Anupam_amount=df.loc[(df["Anupam Investment Amount"].isnull()==False)&(df["Anupam Investment Amount"]!=0)]
Vineeta_amount=df.loc[(df["Vineeta Investment Amount"].isnull()==False)&(df["Vineeta Investment Amount"]!=0)]
Aman_amount=df.loc[(df["Aman Investment Amount"].isnull()==False)&(df["Aman Investment Amount"]!=0)]
Peyush_amount=df.loc[(df["Peyush Investment Amount"].isnull()==False)&(df["Peyush Investment Amount"]!=0)]
Ghazal_amount=df.loc[(df["Ghazal Investment Amount"].isnull()==False)&(df["Ghazal Investment Amount"]!=0)]
print("-"*60,"\n","Ashneer invested in",len(Ashneer_amount),"number of business in the season.")
print("Namita invested in",len(Namita_amount),"number of business in the season.")
print("Anupam invested in",len(Anupam_amount),"number of business in the season.")
print("Vineeta invested in",len(Vineeta_amount),"number of business in the season.")
print("Aman invested in",len(Aman_amount),"number of business in the season.")
print("Peyush invested in",len(Peyush_amount),"number of business in the season.")
print("Ghazal invested in",len(Ghazal_amount),"number of business in the season.","\n","-"*60)
------------------------------------------------------------ Ashneer invested in 21 number of business in the season. Namita invested in 24 number of business in the season. Anupam invested in 24 number of business in the season. Vineeta invested in 16 number of business in the season. Aman invested in 29 number of business in the season. Peyush invested in 28 number of business in the season. Ghazal invested in 7 number of business in the season. ------------------------------------------------------------
startup_count=[len(Ashneer_amount),len(Namita_amount),len(Anupam_amount),len(Vineeta_amount),len(Aman_amount),len(Peyush_amount),len(Ghazal_amount)]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Ghazal']
dfa= {'Name':name,'Startup_count':startup_count}
plt.figure(figsize=(10,4))
plt.bar(dfa['Name'],dfa['Startup_count'])
plt.xticks(rotation=90,fontsize=15)
plt.xlabel("Name",fontsize=14)
plt.ylabel("Number of Startups",fontsize=14)
for index,d in enumerate(startup_count):
plt.text(x=index , y =d+0.2 , s=f"{d}" , fontdict=dict(fontsize=15))
#plt.tight_layout()
plt.title("Investment in Number of startups",fontsize=15)
plt.show()
#the investments amount done by individual shark
startup_count=[Ashneer_amount["Ashneer Investment Amount"].sum(),Namita_amount["Namita Investment Amount"].sum(),Anupam_amount["Anupam Investment Amount"].sum(),Vineeta_amount["Vineeta Investment Amount"].sum(),Aman_amount["Aman Investment Amount"].sum(),Peyush_amount["Peyush Investment Amount"].sum(),Ghazal_amount["Ghazal Investment Amount"].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Ghazal']
dfa = {'Name':name,'Startup_count':startup_count}
plt.figure(figsize=(10,4))
plt.bar(dfa['Name'],dfa['Startup_count'])
plt.xticks(rotation=90,fontsize=15)
plt.xlabel("Name",fontsize=14)
plt.ylabel("Amount in Lakh",fontsize=14)
for index,d in enumerate(startup_count):
plt.text(x=index , y =d+1 , s=f"{round(d,2)}" ,ha = 'center', fontdict=dict(fontsize=12))
#plt.tight_layout()
plt.title("Investments done by individual shark ",fontsize=15)
plt.show()
#equity %
equity=[df['Ashneer Investment Equity'].sum(), df['Namita Investment Equity'].sum(), df['Anupam Investment Equity'].sum(), df['Vineeta Investment Equity'].sum(),
df['Aman Investment Equity'].sum(), df['Peyush Investment Equity'].sum(), df['Ghazal Investment Equity'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Ghazal']
df = {'Name':name,'Total equity':equity }
plt.figure(figsize=(10,4))
plt.bar(df['Name'],df['Total equity'])
plt.xticks(rotation=90,fontsize=15)
plt.xlabel("Name",fontsize=14)
plt.ylabel("Sum of equity percentage in different companies",fontsize=14)
for index,d in enumerate(equity):
plt.text(x=index , y =d+2 , s=f"{round(d,2)}" ,ha = 'center', fontdict=dict(fontsize=12))
#plt.tight_layout()
plt.title("Total equity percentage of individual shark ",fontsize=15)
plt.show()
#investment based on the Debt and loaned Amount
debt=[df['Ashneer Debt Amount'].sum(), df['Namita Debt Amount'].sum(), df['Anupam Debt Amount'].sum(), df['Vineeta Debt Amount'].sum(),
df['Aman Debt Amount'].sum(), df['Peyush Debt Amount'].sum(), df['Ghazal Debt Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Ghazal']
dfa = {'Name':name,'Total debt':debt }
plt.figure(figsize=(10,4))
plt.bar(dfa['Name'],dfa['Total debt'])
plt.xticks(rotation=90,fontsize=15)
plt.xlabel("Name",fontsize=14)
plt.ylabel("Total debt amount in lakh",fontsize=14)
for index,d in enumerate(debt):
plt.text(x=index , y =d+2 , s=f"{round(d,2)}" ,ha = 'center', fontdict=dict(fontsize=12))
#plt.tight_layout()
plt.title("Debt amount given by individual shark",fontsize=15)
plt.show()
#
DealDone=df.loc[df["Accepted Offer"]==1]
fig = plt.figure(figsize=(15,8))
plt.subplot()
plt.scatter("Total Deal Equity","Total Deal Amount",data=DealDone,s="Valuation Offered",c="Valuation Offered",cmap="flare",edgecolor="k",linewidths=.8)
plt.title("Total Investment and equity",fontsize=20)
plt.xlabel("Equity in %",fontsize=15,color="k")
plt.ylabel("Investmet in Lacks",fontsize=15,color="k")
plt.colorbar(label="valuation in Lacks")
<matplotlib.colorbar.Colorbar at 0x2acdb0ace80>
## Ashneer's Investment Analysis
Ashneer = Ashneer_amount.sort_values('Ashneer Investment Amount')
plt.rcParams.update({'font.size': 16})
plt.figure(figsize=(35,20))
plt.subplot(2,2,1)
plt.barh(Ashneer["Startup Name"],Ashneer["Ashneer Investment Amount"])
for i, v in enumerate(Ashneer["Ashneer Investment Amount"]):
plt.text(v, i , str(round(v))+"L", color='black', fontweight='bold')
plt.title("Ashneer Investment Amount")
plt.subplot(2,2,2)
plt.barh(Ashneer["Startup Name"],Ashneer["Ashneer Investment Equity"])
for i, v in enumerate(Ashneer["Ashneer Investment Equity"]):
plt.text(v, i , str(round(v))+"%", color='black', fontweight='bold')
plt.title("Ashneer Investment Equity")
plt.subplot(2,2,3)
Ashneer["Industry"].value_counts().plot(kind='pie',autopct='%.2f%%',shadow=True)
plt.title("Industry")
plt.subplot(2,2,4)
plt.barh(Ashneer["Startup Name"],Ashneer["Number of sharks in deal"])
for i, v in enumerate(Ashneer["Number of sharks in deal"]):
plt.text(v, i , str(round(v)), color='black', fontweight='bold')
plt.title("Number of sharks in deal")
plt.suptitle("Ashneer's Investment Analysis",fontsize=40)
Text(0.5, 0.98, "Ashneer's Investment Analysis")
Namita = Namita_amount.sort_values('Namita Investment Amount')
plt.rcParams.update({'font.size': 16})
plt.figure(figsize=(35,20))
plt.subplot(2,2,1)
plt.barh(Namita["Startup Name"],Namita["Namita Investment Amount"])
for i, v in enumerate(Namita["Namita Investment Amount"]):
plt.text(v, i , str(round(v))+"L", color='black', fontweight='bold')
plt.title("Namita Investment Amount")
plt.subplot(2,2,2)
plt.barh(Namita["Startup Name"],Namita["Namita Investment Equity"])
for i, v in enumerate(Namita["Namita Investment Equity"]):
plt.text(v, i , str(round(v))+"%", color='black', fontweight='bold')
plt.title("Namita Investment Equity")
plt.subplot(2,2,3)
Namita["Industry"].value_counts().plot(kind='pie',autopct='%.2f%%',shadow=True)
plt.title("Industry")
plt.subplot(2,2,4)
plt.barh(Namita["Startup Name"],Namita["Number of sharks in deal"])
for i, v in enumerate(Namita["Number of sharks in deal"]):
plt.text(v, i , str(round(v)), color='black', fontweight='bold')
plt.title("Number of sharks in deal")
plt.suptitle("Namita's Investment Analysis",fontsize=40)
Text(0.5, 0.98, "Namita's Investment Analysis")
Anupam = Anupam_amount.sort_values('Anupam Investment Amount')
plt.rcParams.update({'font.size': 15})
plt.figure(figsize=(38,25))
plt.subplot(2,2,1)
plt.barh(Anupam["Startup Name"],Anupam["Anupam Investment Amount"])
for i, v in enumerate(Anupam["Anupam Investment Amount"]):
plt.text(v, i , str(round(v))+"L", color='black', fontweight='bold')
plt.title("Anupam Investment Amount")
plt.subplot(2,2,2)
plt.barh(Anupam["Startup Name"],Anupam["Anupam Investment Equity"])
for i, v in enumerate(Anupam["Anupam Investment Equity"]):
plt.text(v, i , str(round(v))+"%", color='black', fontweight='bold')
plt.title("Anupam Investment Equity")
plt.subplot(2,2,3)
Anupam["Industry"].value_counts().plot(kind='pie',autopct='%.2f%%',shadow=True)
plt.title("Industry")
plt.subplot(2,2,4)
plt.barh(Anupam["Startup Name"],Anupam["Number of sharks in deal"])
for i, v in enumerate(Anupam["Number of sharks in deal"]):
plt.text(v, i , str(round(v)), color='black', fontweight='bold')
plt.title("Number of sharks in deal")
plt.suptitle("Anupam's Investment Analysis",fontsize=40)
Text(0.5, 0.98, "Anupam's Investment Analysis")
Vineeta = Vineeta_amount.sort_values('Vineeta Investment Amount')
plt.rcParams.update({'font.size': 16})
plt.figure(figsize=(35,20))
plt.subplot(2,2,1)
plt.barh(Vineeta["Startup Name"],Vineeta["Vineeta Investment Amount"])
for i, v in enumerate(Vineeta["Vineeta Investment Amount"]):
plt.text(v, i , str(round(v))+"L", color='black', fontweight='bold')
plt.title("Vineeta Investment Amount")
plt.subplot(2,2,2)
plt.barh(Vineeta["Startup Name"],Vineeta["Vineeta Investment Equity"])
for i, v in enumerate(Vineeta["Vineeta Investment Equity"]):
plt.text(v, i , str(round(v))+"%", color='black', fontweight='bold')
plt.title("Vineeta Investment Equity")
plt.subplot(2,2,3)
Vineeta["Industry"].value_counts().plot(kind='pie',autopct='%.2f%%',shadow=True)
plt.title("Industry")
plt.subplot(2,2,4)
plt.barh(Vineeta["Startup Name"],Vineeta["Number of sharks in deal"])
for i, v in enumerate(Vineeta["Number of sharks in deal"]):
plt.text(v, i , str(round(v)), color='black', fontweight='bold')
plt.title("Number of sharks in deal")
plt.suptitle("Vineeta's Investment Analysis",fontsize=40)
Text(0.5, 0.98, "Vineeta's Investment Analysis")
Aman = Aman_amount.sort_values('Aman Investment Amount')
plt.rcParams.update({'font.size': 16})
plt.figure(figsize=(35,20))
plt.subplot(2,2,1)
plt.barh(Aman["Startup Name"],Aman["Aman Investment Amount"])
for i, v in enumerate(Aman["Aman Investment Amount"]):
plt.text(v, i , str(round(v))+"L", color='black', fontweight='bold')
plt.title("Aman Investment Amount")
plt.subplot(2,2,2)
plt.barh(Aman["Startup Name"],Aman["Aman Investment Equity"])
for i, v in enumerate(Aman["Aman Investment Equity"]):
plt.text(v, i , str(round(v))+"%", color='black', fontweight='bold')
plt.title("Aman Investment Equity")
plt.subplot(2,2,3)
Aman["Industry"].value_counts().plot(kind='pie',autopct='%.2f%%',shadow=True)
plt.title("Industry")
plt.subplot(2,2,4)
plt.barh(Aman["Startup Name"],Aman["Number of sharks in deal"])
for i, v in enumerate(Aman["Number of sharks in deal"]):
plt.text(v, i , str(round(v)), color='black', fontweight='bold')
plt.title("Number of sharks in deal")
plt.suptitle("Aman's Investment Analysis",fontsize=40)
Text(0.5, 0.98, "Aman's Investment Analysis")
##Peyush's Investment Analysis
Peyush = Peyush_amount.sort_values('Peyush Investment Amount')
plt.rcParams.update({'font.size': 16})
plt.figure(figsize=(35,20))
plt.subplot(2,2,1)
plt.barh(Peyush["Startup Name"],Peyush["Peyush Investment Amount"])
for i, v in enumerate(Peyush["Peyush Investment Amount"]):
plt.text(v, i , str(round(v))+"L", color='black', fontweight='bold')
plt.title("Peyush Investment Amount")
plt.subplot(2,2,2)
plt.barh(Peyush["Startup Name"],Peyush["Peyush Investment Equity"])
for i, v in enumerate(Peyush["Peyush Investment Equity"]):
plt.text(v, i , str(round(v))+"%", color='black', fontweight='bold')
plt.title("Peyush Investment Equity")
plt.subplot(2,2,3)
Peyush["Industry"].value_counts().plot(kind='pie',autopct='%.2f%%',shadow=True)
plt.title("Industry")
plt.subplot(2,2,4)
plt.barh(Peyush["Startup Name"],Peyush["Number of sharks in deal"])
for i, v in enumerate(Peyush["Number of sharks in deal"]):
plt.text(v, i , str(round(v)), color='black', fontweight='bold')
plt.title("Number of sharks in deal")
plt.suptitle("Peyush's Investment Analysis",fontsize=40)
Text(0.5, 0.98, "Peyush's Investment Analysis")
Ghazal = Ghazal_amount.sort_values('Ghazal Investment Amount')
plt.rcParams.update({'font.size': 16})
plt.figure(figsize=(35,20))
plt.subplot(2,2,1)
plt.barh(Ghazal["Startup Name"],Ghazal["Ghazal Investment Amount"])
for i, v in enumerate(Ghazal["Ghazal Investment Amount"]):
plt.text(v, i , str(round(v))+"L", color='black', fontweight='bold')
plt.title("Ghazal Investment Amount")
plt.subplot(2,2,2)
plt.barh(Ghazal["Startup Name"],Ghazal["Ghazal Investment Equity"])
for i, v in enumerate(Ghazal["Ghazal Investment Equity"]):
plt.text(v, i , str(round(v))+"%", color='black', fontweight='bold')
plt.title("Ghazal Investment Equity")
plt.subplot(2,2,3)
Ghazal["Industry"].value_counts().plot(kind='pie',autopct='%.2f%%',shadow=True)
plt.title("Industry")
plt.subplot(2,2,4)
plt.barh(Ghazal["Startup Name"],Ghazal["Number of sharks in deal"])
for i, v in enumerate(Ghazal["Number of sharks in deal"]):
plt.text(v, i , str(round(v)), color='black', fontweight='bold')
plt.title("Number of sharks in deal")
plt.suptitle("Ghazal's Investment Analysis",fontsize=40)
Text(0.5, 0.98, "Ghazal's Investment Analysis")
